AI Predictions vs Reality: Uncover 7 surprising AI advancements in the past 30 years, from machin...
AI Predictions vs Reality: 7 Surprising Advances in the Last 30 Years
Understanding Text Annotation: Key Types and Their Applications in AI and NLP
Discover the key types of text annotation - entity recognition, sentiment analysis, intent detect...
Supervised Fine-Tuning and Data Labeling: A Human-Centric Approach to AI Training
Learn how fine-tuning adjusts models for specific tasks and how human-labeled data helps guide AI...
Top Sources for Off-the-Shelf Call Center Datasets: A Comprehensive Guide
Discover the top sources for off-the-shelf call center datasets in this comprehensive guide. Lear...
Will LLMs and LAMs Ever Be Able to Self-Train? What Does That Mean for Humans?
This article examines the potential for Large Language Models (LLMs) and Large Action Models (LAM...
5 Reasons Why Real Conversations are Important for LLMs
Learn why real conversations are crucial for training effective Large Language Models (LLMs). Exp...
Off-the-Shelf vs. Custom Conversational Datasets: A Comparative Analysis
Discover the differences between Off-The-Shelf (OTS) datasets and custom collections in our lates...
The Power of Custom Conversational Datasets: A Superior Alternative to Off-the-Shelf Solutions
This article explores the advantages of custom conversational datasets over off-the-shelf (OTS) s...
Applications and Future Prospects of Large Action Models
Explore the transformative potential of Large Action Models (LAMs) in various industries, includi...
The Training Process of Large Action Models (LAMs)
Explore the intricate process of training Large Action Models, focusing on their application in t...
How Data Collection is Done for AI Model Training
Discover how data collection is crucial for training AI models. Learn about data sources, preproc...
Understanding Large Action Models (LAMs)
This article explores the key characteristics and applications of LAMs, which excel in planning,...
